Memory types · Working memory
Short-Term Memory in AI Agents
Short-term memory in AI agents is the information held in the current task and recent turns — primarily inside the context window — before it is consolidated into long-term storage or discarded.
Short-term lifecycle
Definition
What is short-term (working) memory in agents?
Working memory is the active scratchpad for the current goal, recent messages and tool outputs — living in the context window plus small buffers.
It is volatile: cleared when the session ends unless promoted to long-term storage. Also called “context memory” in some frameworks — but that term often confuses working memory with persistent AI memory; see memory vs context window.
Mechanism
How short-term memory works in practice
Messages accumulate in the prompt; when the window fills, agents summarize, truncate or page out oldest turns.
Framework buffers include LangChain ConversationBuffer and LangGraph checkpoint state for recent turns. Tool results inject directly into working memory for the current reasoning step.
Constraints
Limits of short-term memory
Token caps, truncation of oldest turns, per-token cost and no cross-session persistence by default.
GPT-4: 128K tokens; Claude 3.7 Sonnet: 200K (as cited in Chhikara et al., 2025). When the limit hits: summarize, drop or promote to long-term memory.
Transition
Promoting short-term to long-term memory
Extraction → consolidation → external store.
Promote when a fact is durable (preferences, identity, recurring context); discard when ephemeral (greeting, one-off clarification).
Comparison
Short-term vs long-term memory
| Dimension | Short-term | Long-term |
|---|---|---|
| Location | Context window | External store |
| Persistence | Session | Cross-session |
| Capacity | Model token limit | Unbounded (store) |
| Cost | Per token in window | Per embed + retrieve |
Patterns
Frameworks and patterns for working memory
Context window only; buffer + summarization; Letta paging tiers.
- Context window only — simplest; no persistence across sessions
- Buffer + summarization — LangChain buffers compress old turns
- Letta paging — core memory in window, rest paged to deep store (MemGPT pattern)
- External memory + window — Engram, Mem0 and Zep retrieve into the window each turn; working memory stays in-context
FAQ
Frequently asked questions
What is working memory in AI agents?
Working memory is the active information in the context window for the current task — recent messages, tool outputs and retrieved memories. It is volatile and session-bound. See definition above.
Is short-term memory the same as the context window?
Mostly yes for agents — the context window is the primary short-term/working memory tier, often plus small buffers. Persistent memory lives outside the window. See memory vs context window.
Should I use LSTM for agent memory?
No. LSTM is a neural network architecture for sequence modeling (Hochreiter & Schmidhuber, 1997), not agent working memory. Agents use context windows and external memory stores, not LSTM layers for conversation memory.
What does context memory mean?
Usually information in the context window (working memory), not persistent cross-session storage. For durable memory, use an external store. See memory vs context window.
What is role context memory?
Role context is the system prompt and persona in the window — instructions for how the agent behaves, not facts about users. User facts belong in external long-term memory.
How long does short-term memory last?
For the current session only — until the context window is cleared, the session ends or facts are promoted to long-term storage. No cross-session persistence by default.
When should I promote short-term to long-term memory?
When a fact is durable: preferences, identity, recurring context. Discard ephemeral turns (greetings, clarifications). See memory consolidation.
Does Claude have short-term memory?
Claude's API context window holds short-term/working memory per call. Cross-session persistence requires external memory (Engram, Mem0, Zep, etc.). See persist conversation memory.
How does n8n handle agent short-term memory?
n8n passes conversation history in the prompt (working memory). For persistence across sessions, wire Engram, Mem0 or Zep APIs. See add memory to an agent.
Buffer memory vs vector memory?
Buffer memory keeps recent turns in the prompt (short-term). Vector memory stores embeddings in an external index (long-term). Production agents use both. See long-term memory.